SoroushVahidi/scidocs huggingface dataset:
FIRST AND SECOND ORDER METHODS FOR ONLINE CONVOLUTIONAL
(2018)

Convolutional sparse representations are a form of sparse representation with a structured, translation invariant dictionary. Most convolutional dictionary learning algorithms to date operate in batch mode, requiring simultaneous access to all training images during the learning process, which results in very high memory usage, and severely limits the training data that can be used. Very recently, however, a number of authors have considered the design of online convolutional dictionary learning algorithms that offer far better scaling of memory and computational cost with training set size than batch methods. This paper extends our prior work, improving a number of aspects of our previous algorithm; proposing an entirely new one, with better performance, and that supports the inclusion of a spatial mask for learning from incomplete data; and providing a rigorous theoretical analysis of these methods.
Interactive multiple anisotropic scattering in clouds
(2008)

We propose an algorithm for the real time realistic simulation of multiple anisotropic scattering of light in a volume. Contrary to previous real-time methods we account for all kinds of light paths through the medium and preserve their anisotropic behavior.
 Our approach consists of estimating the energy transport from the illuminated cloud surface to the rendered cloud pixel for each separate order of multiple scattering. We represent the distribution of light paths reaching a given viewed cloud pixel with the mean and standard deviation of their entry points on the lit surface, which we call the collector area. At rendering time for each pixel we determine the collector area on the lit cloud surface for different sets of scattering orders, then we infer the associated light transport. The fast computation of the collector area and light transport is possible thanks to a preliminary analysis of multiple scattering in plane-parallel slabs and does not require slicing or marching through the volume.
 Rendering is done efficiently in a shader on the GPU, relying on a cloud surface mesh augmented with a Hypertexture to enrich the shape and silhouette. We demonstrate our model with the interactive rendering of detailed animated cumulus and cloudy sky at 2--10 frames per second.
On label dependence and loss minimization in multi-label classification
(2012)

Most of the multi-label classification (MLC) methods proposed in recent years intended to exploit, in one way or the other, dependencies between the class labels. Comparing to simple binary relevance learning as a baseline, any gain in performance is normally explained by the fact that this method is ignoring such dependencies. Without questioning the correctness of such studies, one has to admit that a blanket explanation of that kind is hiding many subtle details, and indeed, the underlying mechanisms and true reasons for the improvements reported in experimental studies are rarely laid bare. Rather than proposing yet another MLC algorithm, the aim of this paper is to elaborate more closely on the idea of exploiting label dependence, thereby contributing to a better understanding of MLC. Adopting a statistical perspective, we claim that two types of label dependence should be distinguished, namely conditional and marginal dependence. Subsequently, we present three scenarios in which the exploitation of one of these types of dependence may boost the predictive performance of a classifier. In this regard, a close connection with loss minimization is established, showing that the benefit of exploiting label dependence does also depend on the type of loss to be minimized. Concrete theoretical results are presented for two representative loss functions, namely the Hamming loss and the subset 0/1 loss. In addition, we give an overview of state-of-the-art decomposition algorithms for MLC and we try to reveal the reasons for their effectiveness. Our conclusions are supported by carefully designed experiments on synthetic and benchmark data.
Automated and safe vulnerability assessment
(2005)

As the number of system vulnerabilities multiplies in recent years, vulnerability assessment has emerged as a powerful system security administration tool that can identify vulnerabilities in existing systems before they are exploited. Although there are many commercial vulnerability assessment tools in the market, none of them can formally guarantee that the assessment process never compromises the computer systems being tested. This paper proposes a featherweight virtual machine (FVM) technology to address the safety issue associated with vulnerability testing. Compared with other virtual machine technologies, FVM is designed to facilitate sharing between virtual machines but still provides strong protection between them. The FVM technology allows a vulnerability assessment tool to test an exact replica of a production-mode network service, including both hardware and system software components, while guaranteeing that the production-mode network service is fully isolated from the testing process. In addition to safety, the vulnerability assessment support system described in this paper can also automate the entire process of vulnerability testing and thus for the first time makes it feasible to run vulnerability testing autonomously and frequently. Experiments on a Windows-based prototype show that Nessus assessment results against an FVM virtual machine are identical to those against a real machine. Furthermore, modifications to the file system and registry state made by vulnerability assessment runs are completely isolated from the host machine. Finally, the performance impact of vulnerability assessment runs on production network services is as low as 3%
Understanding Performance Interference of I/O Workload in Virtualized Cloud Environments
(2010)

Server virtualization offers the ability to slice large, underutilized physical servers into smaller, parallel virtual machines (VMs), enabling diverse applications to run in isolated environments on a shared hardware platform. Effective management of virtualized cloud environments introduces new and unique challenges, such as efficient CPU scheduling for virtual machines, effective allocation of virtual machines to handle both CPU intensive and I/O intensive workloads. Although a fair number of research projects have dedicated to measuring, scheduling, and resource management of virtual machines, there still lacks of in-depth understanding of the performance factors that can impact the efficiency and effectiveness of resource multiplexing and resource scheduling among virtual machines. In this paper, we present our experimental study on the performance interference in parallel processing of CPU and network intensive workloads in the Xen Virtual Machine Monitors (VMMs). We conduct extensive experiments to measure the performance interference among VMs running network I/O workloads that are either CPU bound or network bound. Based on our experiments and observations, we conclude with four key findings that are critical to effective management of virtualized cloud environments for both cloud service providers and cloud consumers. First, running network-intensive workloads in isolated environments on a shared hardware platform can lead to high overheads due to extensive context switches and events in driver domain and VMM. Second, co-locating CPU-intensive workloads in isolated environments on a shared hardware platform can incur high CPU contention due to the demand for fast memory pages exchanges in I/O channel. Third, running CPU-intensive workloads and network-intensive workloads in conjunction incurs the least resource contention, delivering higher aggregate performance. Last but not the least, identifying factors that impact the total demand of the exchanged memory pages is critical to the in-depth understanding of the interference overheads in I/O channel in the driver domain and VMM.
Efficient Algorithms for Citation Network Analysis
(2003)

In the paper very efficient, linear in number of arcs, algorithms for determining Hummon and Doreian’s arc weights SPLC and SPNP in citation network are proposed, and some theoretical properties of these weights are presented. The nonacyclicity problem in citation networks is discussed. An approach to identify on the basis of arc weights an important small subnetwork is proposed and illustrated on the citation networks of SOM (self organizing maps) literature and US patents.
Upvoting hurricane Sandy: event-based news production processes on a social news site
(2014)

This paper uses the case of Hurricane Sandy and reddit's topical community (subreddit) /r/sandy to examine the production and curation of news content around events on a social news site. Through qualitative analysis, we provide a coded topology of produced content and describe how types of networked gatekeeping impact the framing of a crisis situation. This study also examines, through quantitative modeling, what kind of information becomes negotiated and voted as relevant. We suggest that highly scored content shared in a social news setting focused more on human-interest media and perspective-based citizen journalism than professional news reports. We conclude by discussing how the mechanisms of social news sites conflict with the social norms and culture of reddit to produce differing expectations around news.
Vehicle detection with sub-class training using R-CNN for the UA-DETRAC benchmark
(2017)

Different types of vehicles, such as buses and cars, can be quite different in shapes and details. This makes it more difficult to try to learn a single feature vector that can detect all types of vehicles using a single object class. We proposed an approach to perform vehicle detection with Sub-Classes categories learning using R-CNN in order to improve the performance of vehicle detection. Instead of using a single object class, which is a vehicle in this experiment, to train on the R-CNN, we used multiple sub-classes of vehicles so that the network can better learn the features of each individual type. In the experiment, we also evaluated the result of using a transfer learning approach to use a pre-trained weights on a new dataset.
An Energy-Efficient Micropower Neural Recording Amplifier
(2007)

This paper describes an ultralow-power neural recording amplifier. The amplifier appears to be the lowest power and most energy-efficient neural recording amplifier reported to date. We describe low-noise design techniques that help the neural amplifier achieve input-referred noise that is near the theoretical limit of any amplifier using a differential pair as an input stage. Since neural amplifiers must include differential input pairs in practice to allow robust rejection of common-mode and power supply noise, our design appears to be near the optimum allowed by theory. The bandwidth of the amplifier can be adjusted for recording either neural spikes or local field potentials (LFPs). When configured for recording neural spikes, the amplifier yielded a midband gain of 40.8 dB and a -3-dB bandwidth from 45 Hz to 5.32 kHz; the amplifier's input-referred noise was measured to be 3.06 muVrms while consuming 7.56 muW of power from a 2.8-V supply corresponding to a noise efficiency factor (NEF) of 2.67 with the theoretical limit being 2.02. When configured for recording LFPs, the amplifier achieved a midband gain of 40.9 dB and a -3-dB bandwidth from 392 mHz to 295 Hz; the input-referred noise was 1.66 muVrms while consuming 2.08 muW from a 2.8-V supply corresponding to an NEF of 3.21. The amplifier was fabricated in AMI's 0.5-mum CMOS process and occupies 0.16 mm2 of chip area. We obtained successful recordings of action potentials from the robust nucleus of the arcopallium (RA) of an anesthesized zebra finch brain with the amplifier. Our experimental measurements of the amplifier's performance including its noise were in good accord with theory and circuit simulations.
A Survey of Algorithmic Shapes
(2015)

In the context of computer-aided design, computer graphics and geometry processing, the idea of generative modeling is to allow the generation of highly complex objects based on a set of formal construction rules. Using these construction rules, a shape is described by a sequence of processing steps, rather than just by the result of all applied operations: shape design becomes rule design. Due to its very general nature, this approach can be applied to any domain and to any shape representation that provides a set of generating functions. The aim of this survey is to give an overview of the concepts and techniques of procedural and generative modeling, as well as their applications with a special focus on archeology and architecture.
Trust in leadership: meta-analytic findings and implications for research and practice.
(2002)

In this study, the authors examined the findings and implications of the research on trust in leadership that has been conducted during the past 4 decades. First, the study provides estimates of the primary relationships between trust in leadership and key outcomes, antecedents, and correlates (k = 106). Second, the study explores how specifying the construct with alternative leadership referents (direct leaders vs. organizational leadership) and definitions (types of trust) results in systematically different relationships between trust in leadership and outcomes and antecedents. Direct leaders (e.g., supervisors) appear to be a particularly important referent of trust. Last, a theoretical framework is offered to provide parsimony to the expansive literature and to clarify the different perspectives on the construct of trust in leadership and its operation.
An Empirical Assessment of Organizational Commitment and Organizational Effectiveness
(2007)

Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.
Social Exchange in Organizations : Perceived Organizational Support , Leader-Member Exchange , and Employee Reciprocity
(2004)

Social exchange (P. Blau, 1964) and the norm of reciprocity (A. W. Gouldner, 1960) have been used to explain the relationship of perceived organizational support and leader-member exchange with employee attitudes and behavior. Recent empirical research suggests that individuals engage in different reciprocation efforts depending on the exchange partner (e.g., B. L. McNeely & B. M. Meglino, 1994). The purpose of the present study was to further investigate these relationships by examining the relative contribution of indicators of employee-organization exchange and subordinate-supervisor exchange. Structural equation modeling was used to compare nested models. Results indicate that perceived organizational support is associated with organizational commitment, whereas leader-member exchange is associated with citizenship and in-role behavior.
What we know about leadership. Effectiveness and personality.
(1994)

Although psychologists know a great deal about leadership, persons who make decisions about real leaders seem largely to ignore their accumulated wisdom. In an effort to make past research more accessible, interpretable, and relevant to decision makers, this article defines leadership and then answers nine questions that routinely come up when practical decisions are made about leadership (e.g., whom to appoint, how to evaluate them, when to terminate them.
Justice at the millennium: a meta-analytic review of 25 years of organizational justice research.
(2001)

The field of organizational justice continues to be marked by several important research questions, including the size of relationships among justice dimensions, the relative importance of different justice criteria, and the unique effects of justice dimensions on key outcomes. To address such questions, the authors conducted a meta-analytic review of 183 justice studies. The results suggest that although different justice dimensions are moderately to highly related, they contribute incremental variance explained in fairness perceptions. The results also illustrate the overall and unique relationships among distributive, procedural, interpersonal, and informational justice and several organizational outcomes (e.g., job satisfaction, organizational commitment, evaluation of authority, organizational citizenship behavior, withdrawal, performance). These findings are reviewed in terms of their implications for future research on organizational justice.
Comparison of Feedback Control Techniques for Torque-Vectoring Control of Fully Electric Vehicles
(2014)

Fully electric vehicles (FEVs) with individually controlled powertrains can significantly enhance vehicle response to steering-wheel inputs in both steady-state and transient conditions, thereby improving vehicle handling and, thus, active safety and the fun-to-drive element. This paper presents a comparison between different torque-vectoring control structures for the yaw moment control of FEVs. Two second-order sliding-mode controllers are evaluated against a feedforward controller combined with either a conventional or an adaptive proportional-integral-derivative (PID) controller. Furthermore, the potential performance and robustness benefits arising from the integration of a body sideslip controller with the yaw rate feedback control system are assessed. The results show that all the evaluated controllers are able to significantly change the understeer behavior with respect to the baseline vehicle. The PID-based controllers achieve very good vehicle performance in steady-state and transient conditions, whereas the controllers based on the sliding-mode approach demonstrate a high level of robustness against variations in the vehicle parameters. The integrated sideslip controller effectively maintains the sideslip angle within acceptable limits in the case of an erroneous estimation of the tire-road friction coefficient.
Using the Delphi method
(2011)

The reliability and validity of the selected research method are subjects to which every researcher is bound to address himself when representing the findings and conclusions of his/her work. In this study we will discuss the reliability, validity and philosophical aspects of the Delphi method, first with a small literature review and then by representing two different surveys conducted using the Delphi method. The point of view in our report is the usability of Delphi in collecting qualitative data for software engineering research. The most significant features of the Delphi method are its recursion and the possibility to get immediate feedback and evaluate one's own answer. Although there are many forms of Delphi techniques, these features exist in one form or the other in all Delphi variations. In Delphi-based surveys, the minimum number of participants is smaller and participants are not selected at random but because of their particular expertise. Among the traditional research methods this is seen to cause the risk of bias and endangering both reliability and validity. In the Delphi method, the recursion produced by three or more rounds, the expertise and - in most cases - anonymity of participants, and the opportunity to evaluate and argue one's own answer after having seen the other opinions and arguments are thought to guarantee the quality of well-planned and well-conducted research.
Analysis on 2-element insertion sort algorithm
(2010)

This paper proposes a 2-element insertion sort algorithm, an improved algorithm on the direct insertion sort, gives the algorithm design idea and the description in C. After analyzes comparatively the time complexity and space complexity of the algorithms, the paper summarizes the pros and cons of the 2-element insertion sort algorithm compared with the original one, so as to giving a theoretical basis for the direct insertion sort algorithm optimization, and playing a guiding role in teaching the relevant chapters in “Data Structure” curriculum.
OPTIMAL PATHS FOR A CAR THAT GOES BOTH FORWARDS AND BACKWARDS
(1990)

The path taken by a car with a given minimum turning radius has a lower bound on its radius of curvature at each point, but the path has cusps if the car shifts into or out of reverse gear. What is the shortest such path a car can travel between two points if its starting and ending directions are specified? One need consider only paths with at most 2 cusps or reversals. We give a set of paths which is sufficient in the sense that it always contains a shortest path and small in the sense that there are at most 68, but usually many fewer paths in the set for any pair of endpoints and directions. We give these paths by explicit formula. Calculating the length of each of these paths and selecting the (not necessarily unique) path with smallest length yields a simple algorithm for a shortest path in each case. These optimal paths or geodesies may be described as follows: If C is an arc of a circle of the minimal turning radius and S is a line segment, then it is sufficient to consider only certain paths of the form CCSCC where arcs and segments fit smoothly, one or more of the arcs or segments may vanish, and where reversals, or equivalently cusps, between arcs or segments are allowed. This contrasts with the case when cusps are not allowed, where Dubins (1957) has shown that paths of the form CCC and CSC suffice.
Improving classification with latent variable models by sequential constraint optimization
(2004)

In this paper we propose a method to use multiple generative models with latent variables for classi cation tasks. The standard approach to use generative models for classi cation is to train a separate model for each class. A novel data point is then classi ed by the model that attributes the highest probability. The algorithm we propose modi es the parameters of the models to improve the classi cation accuracy. Our approach is made computationally tractable by assuming that each of the models is deterministic, by which we mean that a data-point is associated to only a single latent state. The resulting algorithm is a variant of the support vector machine learning algorithm and in a limiting case the method is similar to the standard perceptron learning algorithm. We apply the method to two types of latent variable models. The rst has a discrete latent state space and the second, principal component analysis, has a continuous latent state space. We compare the e6ectiveness of both approaches on a handwritten digit recognition problem and on a satellite image recognition problem. c © 2003 Elsevier B.V. All rights reserved.
Personalized PageRank Based Multi-document Summarization
(2008)

This paper presents a novel multi-document summarization approach based on personalized pagerank (PPRSum). In this algorithm, we uniformly integrate various kinds of information in the corpus. At first, we train a salience model of sentence global features based on Naive Bayes Model. Secondly, we generate a relevance model for each corpus utilizing the query of it. Then, we compute the personalized prior probability for each sentence in the corpus utilizing the salience model and the relevance model both. With the help of personalized prior probability, a Personalized PageRank ranking process is performed depending on the relationships among all sentences in the corpus. Additionally, the redundancy penalty is imposed on each sentence. The summary is produced by choosing the sentences with both high query-focused information richness and high information novelty. Experiments on DUC2007 are performed and the ROUGE evaluation results show that PPRSum ranks between the 1st and the 2nd systems on DUC2007 main task.